Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration
Ziwei Luo, Jing Hu, Xin Wang, Shu Hu, Bin Kong, Youbing Yin, Qi Song,, Xi Wu, Siwei Lyu

TL;DR
This paper introduces SPAC, a reinforcement learning framework for unsupervised deformable image registration that effectively handles large deformations through a step-wise warping process, outperforming existing methods.
Contribution
The paper proposes a novel RL-based registration method with a low-dimensional 'Plan' concept, enabling effective high-dimensional action generation for large deformation registration.
Findings
Achieves significant improvements over state-of-the-art methods.
Handles large deformations effectively in 2D and 3D datasets.
Operates in an end-to-end unsupervised manner.
Abstract
Large deformations of organs, caused by diverse shapes and nonlinear shape changes, pose a significant challenge for medical image registration. Traditional registration methods need to iteratively optimize an objective function via a specific deformation model along with meticulous parameter tuning, but which have limited capabilities in registering images with large deformations. While deep learning-based methods can learn the complex mapping from input images to their respective deformation field, it is regression-based and is prone to be stuck at local minima, particularly when large deformations are involved. To this end, we present Stochastic Planner-Actor-Critic (SPAC), a novel reinforcement learning-based framework that performs step-wise registration. The key notion is warping a moving image successively by each time step to finally align to a fixed image. Considering that it…
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Code & Models
Videos
Taxonomy
TopicsAdvanced Neural Network Applications · Medical Image Segmentation Techniques · Cell Image Analysis Techniques
MethodsAverage Pooling · Convolution · Dilated Convolution · 1x1 Convolution · Global Average Pooling · Switchable Atrous Convolution
